Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes
Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes
复制标题
将陆地卫星观测与水文建模相结合，改进小湖泊地表水监测
DOI：
10.1016/j.jhydrol.2018.08.076
复制
发表时间：
2018
影响因子：
6.4
通讯作者：
R. Calvez
中科院分区：
文献类型：
--
作者：
A. Ogilvie;G. Belaud;S. Massuel;M. Mulligan;Patrick Le Goulven;P. Malaterre;R. Calvez
小型水库是半干旱地区数百万农民的重要供水来源，但其水文模型却受到数据缺乏以及降雨强度高度变化和局部化的影响。卫星图像可用性的增加提供了大量机会，但地表水资源的监测受到小型水库规模小和洪水快速下降的限制。为了克服遥感和水文建模的困难，研究了结合现场数据、数值建模和卫星观测来监测小型水库的好处。 1999 年至 2014 年，以大量实地数据为基础，为半干旱突尼斯的 7 个小型水库（1-10 公顷）开发了每日降雨径流和水平衡耦合模型。使用 546 个 Landsat TM、ETM+ 和 OLI 传感器上的 MNDWI 分类的地表水观测结果，通过 Ensemble (n=100) Kalman Filter 在 15 年期间更新模型输出。集成卡尔曼滤波器提供近乎实时的校正，通过调节错误建模的降雨事件来减少径流误差，同时补偿陆地卫星有限的时间分辨率并校正分类异常值。根据长期水文测量现场数据进行验证，与初始模型预测相比，7 个湖泊的每日体积均方根误差 (RMSE) 下降了 54%，达到 31 200 立方米。该方法再现了重大洪水的幅度和时间及其衰退阶段，为改善小水体洪水动态的水文监测（NSE 从 0.64 增加到 0.94）提供了有价值的方法。在最小且数据稀缺的湖泊中，更高的时间和空间分辨率时间序列对于提高监测精度至关重要。
Small reservoirs represent a critical water supply to millions of farmers across semi-arid regions, but their hydrological modelling suffers from data scarcity and highly variable and localised rainfall intensities. Increased availability of satellite imagery provide substantial opportunities but the monitoring of surface water resources is constrained by the small size and rapid flood declines in small reservoirs. To overcome remote sensing and hydrological modelling difficulties, the benefits of combining field data, numerical modelling and satellite observations to monitor small reservoirs were investigated. Building on substantial field data, coupled daily rainfall-runoff and water balance models were developed for 7 small reservoirs (1–10 ha) in semi arid Tunisia over 1999–2014. Surface water observations from MNDWI classifications on 546 Landsat TM, ETM+ and OLI sensors were used to update model outputs through an Ensemble (n = 100) Kalman Filter over the 15 year period. The Ensemble Kalman Filter, providing near-real time corrections, reduced runoff errors by modulating incorrectly modelled rainfall events, while compensating for Landsat’s limited temporal resolution and correcting classification outliers. Validated against long term hydrometric field data, daily volume root mean square errors (RMSE) decreased by 54% to 31 200 m3across 7 lakes compared to the initial model forecast. The method reproduced the amplitude and timing of major floods and their decline phases, providing a valuable approach to improve hydrological monitoring (NSE increase from 0.64 up to 0.94) of flood dynamics in small water bodies. In the smallest and data-scarce lakes, higher temporal and spatial resolution time series are essential to improve monitoring accuracy.